In the analysis of time series, it is frequent to classify perturbations as Additive Outliers (AO), Innovative Outliers (IO), Level Shift (LS) outliers or Transitory Change (TC) outliers. In this paper, a new outlier type, the Seasonal Level Shift (SLS), is introduced in order to complete the...

The paper deals with estimation of missing observations in possibly nonstationary ARIMA models. First, the model is assumed known, and the structure of the interpolation filter is analysed. Using the inverse or dual autocorrelation function it is seen how estimation of a missing observation is...

The Seasonal Adjustment Research Appraisal committee was created in Italy to evaluate procedures for seasonal adjustment of economic series. Because the TRAMO-SEATS programs were one of the main procedures considered, the committee sent a selection of 11 series of interest to be analysed. This...

The paper contains some implications for applied econometric research. Two important ones are, first, that invertible models, such as AR or VAR models, cannot in general be used to model seasonally adjusted or detrended data. The second one is that to look at the business cycle in detrended...

This paper "tests" the performance of the approaches of Watson (1993), DeJong, Ingram and Whiteman (1996), Canova and De Nicolo (1995) and Ortega (1998) for evaluating stochastic dynamic general equilibrium models using Monte Carlo techniques. It asks: Do different model evaluation methodologies...

This paper develops a formal framework based on multivariate spectral techniques for assessing the performance of multivariate dynamic models whose solution is approximated through simulation. The approach is especially suitable for models that focus on a particular frequency range , such as...

Hodrick-Prescott (HP) Filter of (most often, seasonally adjusted) quaterly series is analysed. Some of the criticism to the filter are adressed. It is seen that, while filtering strongly affects autocorrelations, it has little effect on crosscorrelations. It is argued that the criticism that HP...

In this paper we address the issue of the efficient estimation of the cointegrating vector in linear regression models with variables that follow general (higher order and fractionally) integrated processes.

Brief summaries and user instruction are presented for the programs TRAMO ("Time Series regression with ARIMA Noise, Missing Observations and Outlers") and SEATS ("Signal Extraction in ARIMA Time Series").